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Please be aware that Analyse-it is only available for Microsoft Windows.
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Arranging the dataset

Data in existing Excel worksheets can be used and should be arranged in a List dataset layout. The dataset must contain two continuous scale variables.

When entering new data we recommend using New Dataset to create a new 2 variables dataset ready for data entry.

Using the test

To start the test:

Excel 2007:Select any cell in the range containing the dataset to analyse, then click Correlation on the Analyse-it tab, then click Pearson.

Excel 97, 2000, 2002 & 2003:Select any cell in the range containing the dataset to analyse, then click Analyse on the Analyse-it toolbar, click Correlation then clickPearson.

Click Variable X and Variable Y and select the variables.

Click Alternative hypothesis and select the alternative hypothesis to test.

r ≠ 0 to test if the variables are correlated.

r > 0 to test if the variables are positively correlated, where observations of the variables tend to increase together.

r < 0 to test if the variables are negatively correlated, where observations of one variable tend to increase as observations in the other variable decrease.

Enter Confidence interval to calculate around the Pearson r statistic. The level should be entered as a percentage between 50 and 100, without the % sign.

Click OK to run the test.

The report shows the number of observations analysed, and, if applicable, how many missing cases were pairwise deleted.

The Pearson r correlation statistic and confidence interval are shown.

METHOD The confidence interval is calculated using the Fisher's Normal transformation (see [1] or [2]).

The hypothesis test is shown. The p-value is the probability of rejecting the null hypothesis, that the variables are independent, when it is in fact true. A significant p-value implies that the two variables are correlated.

METHOD The p-value is calculated using the t approximation (see [1]).

The scatter plot (see below) shows a visual assessment of the strength of association.